Comparative analysis to study the Darcy–Forchheimer Tangent hyperbolic flow towards cylindrical surface using artificial neural network: An application to Parabolic Trough Solar Collector

dc.contributor.authorShafiq, Anum
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.date.accessioned2023-11-13T11:59:15Z
dc.date.available2023-11-13T11:59:15Z
dc.date.issued2024en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractSolar thermal collectors convert sunlight into useful thermal energy by absorbing its incoming radiation. Concentrated solar power technologies use the parabolic trough solar collector to collect solar energy with temperatures ranging from 325– 700 K. The tangent hyperbolic fluid model is one of the most important non-Newtonian fluid models. Laboratory studies demonstrate that this model accurately predicts the shear thinning phenomenon. In addition, tangent hyperbolic fluid has a better heat transfer performance due to its rheological bearing at various shear rates. The current study investigates the heat transmission performance of Darcy–Forchheimer tangent hyperbolic radiative inclined cylindrical film movement in parabolic trough solar collector with an irregular heat sink/source utilizing the Levenberg–Marquardt technique and backpropagated neural networks. Through the implementation of required transformations, this system is turned into an equivalent nonlinear ordinary differential system. The findings are investigated for Newtonian and tangent hyperbolic fluid cases to understand the rheological characteristics. The outcomes are considered using graphical and mathematical evaluations. Fluids featuring tangent hyperbolic rheological conductivity are obligatory for active rate of heat diffusion. As a consequence, these fluids may be employed in Parabolic Trough Solar Collector for increased heat transmission rate and operational usage of solar energy. Furthermore, We create a dataset using the Runge–Kutta fourth-order shooting technique to create the proposed multilayer perceptron artificial neural network. The data points representing the MoD values are observed to be closely clustered around the zero deviation line. Additionally, it is important to highlight that these data points have relatively small numerical values. Moreover, when calculating the average MoD values for each output, it becomes evident that they are consistently very low.en_US
dc.identifier.doi10.1016/j.matcom.2023.09.014en_US
dc.identifier.endpage230en_US
dc.identifier.scopus2-s2.0-85173146369en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage213en_US
dc.identifier.urihttps://hdl.handle.net/11467/7024
dc.identifier.urihttps://doi.org/10.1016/j.matcom.2023.09.014
dc.identifier.volume216en_US
dc.identifier.wosWOS:001094554300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofMathematics and Computers in Simulationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectDarcy–Forchheimer; Parabolic Trough Solar Collector (PTSC); Thermal radiation; Tangent hyperbolic fluid model; Artificial neural networken_US
dc.titleComparative analysis to study the Darcy–Forchheimer Tangent hyperbolic flow towards cylindrical surface using artificial neural network: An application to Parabolic Trough Solar Collectoren_US
dc.typeArticleen_US

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